Method and apparatus for traffic light violation prediction and control

ABSTRACT

A traffic sensor system for detecting and tracking vehicles is described. The disclosed system may be employed as a traffic light violation prediction system for a traffic signal, and as a collision avoidance system. A video camera is employed to obtain a video image of a section of a roadway. Motion is detected through changes in luminance and edges in frames of the video image. Predetermined sets of pixels (“tiles”) in the frames are designated to be in either an “active” state or an “inactive” state. A tile becomes active when the luminance or edge values of the pixels of the tile differ from the respective luminance or edge values of a corresponding tile in a reference frame in accordance with predetermined criteria. The tile becomes inactive when the luminance or edge values of the pixels of the tile do not differ from the corresponding reference frame tile in accordance with the predetermined criteria. Shape and motion of groups of active tiles (“quanta”) are analyzed with software and a neural network to detect and track vehicles.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a Divisional of U.S. Pat. application Ser.No. 09,059,151, filed Apr. 13, 1998, entitled TRAFFIC SENSOR, whichclaims priority to U.S. Provisional Patent Application Ser. No.60/043,690, entitled TRAFFIC SENSOR, filed Apr. 14, 1997.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] N/A

BACKGROUND OF THE INVENTION

[0003] The present invention is related to traffic monitoring systems,and more particularly to a traffic monitoring system for detecting,measuring and anticipating vehicle motion.

[0004] Systems for monitoring vehicular traffic are known. For example,it is known to detect vehicles by employing inductive loop sensors. Atleast one loop of wire or a similar conductive element may be disposedbeneath the surface of a roadway at a predetermined location.Electromagnetic induction occurs when a vehicle occupies the roadwayabove the loop. The induction can be detected via a simple electroniccircuit that is coupled with the loop. The inductive loop and associateddetection circuitry can be coupled with an electronic counter circuit tocount the number of vehicles that pass over the loop. However, inductiveloops are subjected to harsh environmental conditions and consequentlyhave a relatively short expected lifespan.

[0005] It is also known to employ optical sensors to monitor vehiculartraffic. For example, traffic monitoring systems that employ “machinevision” technology such as video cameras are known. Machine visiontraffic monitoring systems are generally mounted above the surface ofthe roadway and have the potential for much longer lifespan thaninductive loop systems. Further, machine vision traffic monitoringsystems have the potential to provide more information about trafficconditions than inductive loop traffic monitoring systems. However,known machine vision traffic monitoring systems have not achieved thesepotentials.

SUMMARY OF THE INVENTION

[0006] In accordance with the present invention, a traffic monitoringstation employs at least one video camera and a computation unit todetect and track vehicles passing through the field of view of the videocamera. The disclosed system may be used as a traffic light violationprediction system for a traffic signal, and/or as a collision avoidancesystem.

[0007] In an illustrative embodiment, the camera provides a video imageof a section of roadway in the form of successive individual videoframes. Motion is detected through edge analysis and changes inluminance relative to an edge reference frame and a luminance referenceframe. The frames are organized into a plurality of sets of pixels. Eachset of pixels (“tile”) is in either an “active” state or an “inactive”state. A tile becomes active when the luminance or edge values of thepixels of the tile differ from the luminance and edge values of thecorresponding tiles in the corresponding reference frames in accordancewith predetermined criteria. The tile becomes inactive when theluminance and edge values of the pixels of the tile do not differ fromthe corresponding reference frame tiles in accordance with thepredetermined criteria.

[0008] The reference frames, which represent the view of the camerawithout moving vehicles, may be dynamically updated in response toconditions in the field of view of the camera. The reference frames areupdated by combining each new frame with the respective referenceframes. The combining calculation is weighted in favor of the referenceframes to provide a gradual rate of change in the reference frames. Aprevious frame may also be employed in a “frame-to-frame” comparisonwith the new frame to detect motion. The frame-to-frame comparison mayprovide improved results relative to use of the reference frame inconditions of low light and darkness.

[0009] Each object is represented by at least one group of proximateactive tiles (“quanta”). Individual quantum, each of which contains apredetermined maximum number of tiles, are tracked through successivevideo frames. The distance traveled by each quantum is readilycalculable from the change in position of the quantum relative tostationary features in the field of view of the camera. The time takento travel the distance is readily calculable since the period of timebetween successive frames is known. Physical parameters such asvelocity, acceleration and direction of travel of the quantum arecalculated based on change in quantum position over time. Physicalparameters that describe vehicle motion are calculated by employing thephysical parameters calculated for the quanta. For example, thevelocities calculated for the quanta that comprise the vehicle may becombined and averaged to ascertain the velocity of the vehicle.

[0010] The motion and shape of quanta are employed to delineate vehiclesfrom other objects. A plurality of segmenter algorithms is employed toperform grouping, dividing and pattern matching functions on the quanta.For example, some segmenter algorithms employ pattern matching tofacilitate identification of types of vehicles, such as passengerautomobiles and trucks. A physical mapping of vehicle models may beemployed to facilitate the proper segmentation of vehicles. A list ofpossible new objects is generated from the output of the segmenteralgorithms. The list of possible new objects is compared with a masterlist of objects, and objects from the list of possible new objects thatcannot be found in the master list are designated as new objects. Theobject master list is then updated by adding the new objects to theobject master list.

[0011] At least one feature extractor is employed to generate adescriptive vector for each object. The descriptive vector is providedto a neural network classification engine which classifies and scoreseach object. The resultant score indicates the probability of the objectbeing a vehicle of a particular type. Objects that produce a score thatexceeds a predetermined threshold are determined to be vehicles.

[0012] The traffic monitoring station may be employed to facilitatetraffic control in real time. Predetermined parameters that describevehicle motion may be employed to anticipate future vehicle motion.Proactive action may then be taken to control traffic in response to theanticipated motion of the vehicle. For example, if on the basis ofstation determined values for vehicle distance from the intersection,speed, acceleration, and vehicle class (truck, car, etc.), the trafficmonitoring station determines that the vehicle will “run a red light,”traversing an intersection during a period of time when the trafficsignal will be otherwise be indicating “green” for vehicles entering theintersection from another direction, the traffic monitoring station candelay the green light for the other vehicles or cause some other actionsto be taken to reduce the likelihood of a collision. Such actions mayalso include displaying the green light for the other vehicles in analtered mode (e.g., flashing) or in some combination with another signallight (e.g., yellow or red), or initiating an audible alarm at theintersection until the danger has passed. Further, the trafficmonitoring station may track the offending vehicle through theintersection and record a full motion video movie of the event forvehicle identification and evidentiary purposes.

BRIEF DESCRIPTION OF THE DRAWING

[0013] The foregoing features of this invention, as well as theinvention itself, may be more fully understood from the followingDetailed Description of the Invention, and Drawing, of which:

[0014]FIG. 1A is a perspective diagram of a traffic monitoring stationthat illustrates configuration;

[0015]FIG. 1B is a side view diagram of a traffic monitoring stationthat illustrates tilt angle;

[0016]FIG. 1C is a top view diagram of a traffic monitoring station thatillustrates pan angle;

[0017]FIG. 2 is a flow diagram that illustrates the vehicle detectionand tracking method of the traffic monitoring station;

[0018]FIG. 3 is a diagram of a new frame that illustrates use of tilesand quanta to identify and track objects;

[0019]FIG. 4 is a diagram of a reference frame;

[0020]FIG. 5 is a diagram that illustrates edge detect tile comparisonto determine tile activation;

[0021]FIG. 6 is a diagram that illustrates adjustment of segmenteralgorithm weighting;

[0022]FIG. 7 is a diagram that illustrates feature vector generation bya feature extractor;

[0023]FIG. 8 is a diagram of the traffic monitoring station of FIG. 1that illustrates the processing module and network connections;

[0024]FIG. 9 is a block diagram of the video capture card of FIG. 8;

[0025]FIG. 10A is a diagram that illustrates use of the new frame forimage stabilization;

[0026]FIG. 10B is a diagram that illustrates use of the reference framefor image stabilization;

[0027]FIG. 11 is diagram of the field of view of a camera thatillustrates use of entry and exit zones;

[0028]FIG. 12 is a block diagram of traffic monitoring stationsnetworked through a graphic user interface;

[0029]FIG. 13 is a flow diagram that illustrates station to stationvehicle tracking; and

[0030]FIG. 14 is a diagram of an intersection that illustrates trafficcontrol based on data gathered by the monitoring station.

DETAILED DESCRIPTION OF THE INVENTION

[0031] U.S. Provisional Patent Application Ser. No. 60/043,690, entitledTRAFFIC SENSOR, filed Apr. 14, 1997, is hereby incorporated herein byreference.

[0032] Referring to FIGS. 1A, 1B and 1C, a traffic monitoring station 8includes at least one camera 10 and a computation unit 12. The camera 10is employed to acquire a video image of a section of a roadway 14. Thecomputation unit 12 is employed to analyze the acquired video images todetect and track vehicles.

[0033] A three dimensional geometric representation of the site iscalculated from parameters entered by the user in order to configure thetraffic monitoring station 8 for operation. The position of a selectedreference feature 16 relative to the camera 10 is measured and enteredinto memory by employing a graphic user interface. In particular, adistance Y along the ground between the camera 10 and the referencefeature 16 on a line that is parallel with the lane markings 17 and adistance X along a line that is perpendicular with the lane markings aremeasured and entered into memory. The camera height H, lane widths ofall lanes W1, W2, W3 and position of each lane in the field of view ofthe camera are also entered into memory. The tilt angle 15 and pan angle13 of the camera are trigonometrically calculated from the user-enteredinformation, such as shown in Appendix A. The tilt angle 15 is the anglebetween a line 2 directly out of the lens of the camera 10 and a line 6that is parallel to the road. The pan angle 13 is the angle between line2 and a line 3 that is parallel to the lane lines and passes directlyunder the camera 10. A value used for scaling (“scaler”) is calculatedfor facilitating distance calculations. The scaler is a fixed factor forthe entire image that is used for conversion between real distances andpixel displacements. Hence, the distance and direction from the camerato any point in the field of view of the camera, and the distance anddirection between any two points in the field of view of the camera canbe determined.

[0034] Corrections for roadway grade and bank may also be calculatedduring configuration. “Grade” refers to the change in height of theroadway relative to the height of the camera within the field of view ofthe camera. “Bank” refers to the difference in height of portions of theroadway along a line perpendicular with the lane markings. The userdetermines the grade and bank of the roadway and enters the determinedvalues into memory by employing a graphic user interface. The grade andbank corrections are achieved by translating the reference plane tomatch the specified grade and bank.

[0035] Referring to FIGS. 2 and 3, operation of the traffic monitoringstation will now be described. A video frame 18 is acquired from thecamera as depicted in step 20. If an interlaced camera is employed, theacquired frame is de-interlaced. If a progressive scan camera isemployed then de-interlacing is not necessary. Image stabilizationtechniques may also be employed to compensate for movement of the cameradue to wind, vibration and other environmental factors, as will bedescribed below. The acquired frame 18 is organized into tiles 22 asdepicted in step 24. Each tile 22 is a region of predetermineddimensions. In the illustrated embodiment, each frame contains 80 tilesper row and 60 tiles per column and the dimensions of each tile are 8pixels by 8 pixels. Tile dimensions may be adjusted, may be non-square,and may overlap other tiles.

[0036] Referring to FIGS. 2, 3 and 4, a list 26 of tiles in which motionis detected (“active tiles”) 38 is generated by employing either or bothof reference frames 28, 29 and a previously acquired frame 30 inseparate comparisons with the acquired frame 18. The reference frame 28represents the luminance of the image from the camera in the absence ofmoving vehicles. The reference frame 29 represents edges detected in theimage from the camera in the absence of moving vehicles. In theillustrated embodiment, both the reference frames 28, 29 and theprevious frame 30 are employed. If a color camera is employed, thechrominance (color) portion of each tile 22 in the acquired frame isseparated from the luminance (black and white) portion prior tocomparison.

[0037] As illustrated in FIG. 5, an edge detect comparison may beemployed to detect motion and activate tiles. For each tile 22 of thenew frame 18 (FIG. 3), the tile is organized into four “quartiles” 32 ofequal size. The pixel luminance values in each quartile 32 are summed toprovide a representative luminance value for each quartile. In theillustrated embodiment, each pixel has a luminance represented by avalue from 0 to 255, where greater values indicate greater luminance.The quartile having the maximum representative luminance value is thenidentified and employed as a baseline for analyzing the other quartiles.In particular, the maximum luminance quartile 34 is designated to be ina first state, illustrated as logic 1. The other quartiles in the tileare designated to be in the first state if their representativeluminance value exceeds a threshold defined by a predeterminedpercentage of the luminance value of the maximum luminance quartile 34(lum≧βlum_(max)). β (“the gain”) can be fixed at a specific level or maybe allowed to vary based upon the characteristics of the image.Quartiles with a representative value that fails to exceed the thresholdare designated to be in a second state, illustrated by logic 0. Eachquartile is then compared with the corresponding quartile from thecorresponding tile 36 from the reference frame 29 (FIG. 4) and,separately, the previously acquired frame. The tile 22 is designated as“active” if the comparison indicates a difference in the state of morethan one quartile. If the comparison indicates a difference in the stateof one or fewer quartiles and at least one quartile of the tile is inthe second state, the tile is designated as “inactive.”

[0038] In the case where each quartile 32 in the corresponding tiles ofthe current frame and the reference frame are designated to be in thefirst state a luminance activation technique is employed. A luminanceintensity value is determined by summing the luminance of all pixels inthe tile and dividing the sum by the total number of pixels in the tile,i.e., computing the average luminance. The average luminance of the tileis compared with the average luminance of the tile 36 in thecorresponding location of the reference frame 28 and the previous frameto detect any difference therebetween. In particular, the averageluminance of the reference tile is subtracted from the average luminanceof the new tile to produce a difference value and, if the magnitude ofthe difference value exceeds a predetermined threshold, motion isindicated and the tile is designated as “active.” The model using tiles,quartiles and pixels is isomorphic to a neural model of several layers.

[0039] Referring again to FIGS. 3 and 4, the reference frames 28, 29 maybe either static or dynamic. A static reference frame may be generatedby storing a video frame from the roadway or portion(s) of the roadwaywhen there are no moving objects in the field of view of the camera. Inthe illustrated embodiment the reference frames 28, 29 are dynamicallyupdated in order to filter differences between frames that areattributable to gradually changing conditions such as shadows. Thereference frames are updated by combining each new frame 18 with thereference frames. The combining calculation may be weighted in favor ofthe reference frames to filter quickly occurring events, such as thepassage of vehicles, while incorporating slowly occurring events such asshadows and changes in the ambient light level.

[0040] Referring to FIGS. 2 and 3, and Appendix B, active tiles 38 inthe list 26 of active tiles are organized into sets of proximatelygrouped active tiles (“quanta”) 40 as depicted by step 42. The quanta 40are employed to track moving objects such as vehicles on successiveframes. The distance traveled by each quantum is calculated based uponthe change in position of the quantum from frame to frame. Matching andidentifying of quantum is facilitated by a “grab phase” and an“expansion phase” as depicted by step 44. Each quantum has a shape. Inthe “grab phase,” active tiles are sought in a predicted position thatis calculated for the quantum in the new frame, within the shape definedby the quantum. The predicted position is determined by the previouslyobserved velocity and direction of travel of the quantum. If any activetiles are located within the quantum shape region in the predictedposition of the quantum in the new frame, the quantum is consideredfound. If no active tiles are located in the quantum shape region in thepredicted position in the new frame, the quantum is considered lost. Inthe “expansion phase,” active tiles that are adjacent to a found quantumand that have not been claimed by other quanta are incorporated into thefound quantum, thereby allowing each quantum to change shape. Unclaimedactive tiles are grouped together to form new quanta unless the numberof active tiles is insufficient to form a quantum. If any of the quantathat have changed shape now exceed a predetermined maximum size thenthese “parent” known quantum are reorganized into a plurality of“children” quantum. Each child quantum inherits the characteristics ofits parent quantum, such as velocity, acceleration and direction.

[0041] The identified quanta are organized into objects as depicted instep 46. The traffic sensor employs a plurality of segmenter algorithmsto organize the identified quanta into objects. Each segmenter algorithmperforms a grouping, dividing or pattern matching function. For example,a “blob segmenter” groups quanta that are connected. Some segmenteralgorithms facilitate identification of types of vehicles, such aspassenger automobiles and trucks. Some segmenter algorithms facilitateidentification of vehicle features such as headlights. Some segmenteralgorithms reorganize groups of quanta to facilitate identification offeatures.

[0042] Referring to FIG. 6, the segmenter algorithms are employed inaccordance with a dynamic weighting technique to facilitate operationunder changing conditions. Five segmenter algorithms designated bynumbers 1-5 are employed in the illustrative example. One segmenteralgorithm is employed in each time slot. In particular, the segmenteralgorithm in the time slot designated by an advancing pointer 48 isemployed. When a segmenter algorithm successfully detects and tracks anobject that is determined to be a vehicle by the neural network and isconsistent across a plurality of frames then that segmenter algorithm isgranted an additional time slot. Consequently, the segmenter algorithmsthat are more successful under the prevailing conditions are weightedmore heavily than the unsuccessful segmenter algorithms. However, eachsegmenter algorithm is assigned at least one permanent time slot 50 inorder to assure that each of the segmenter algorithms remains activewithout regard to performance. Hence, operation dynamically adjusts tochanging conditions to maintain optimum performance. It should beapparent that the number of segmenters, and number and position of thetime slot allocations may be altered from the illustrative example.

[0043] A list of possible new objects represented by their componentquanta is generated by the segmenters as depicted by step 54. The listof possible new objects is compared with a master list of objects, andany objects from the list of possible new objects that cannot be foundin the master list is designated as a new object as depicted by step 56.The object master list is updated by adding the new objects to theobject master list as depicted in step 57. The objects in the updatedobject master list are then classified and scored as depicted in step58.

[0044] Referring to FIGS. 2 and 7, the objects in the master list areexamined by employing at least one feature extractor 49 as depicted bystep 47. Each feature extractor produces a vector 51 of predeterminedlength that describes an aspect of the object, such as shape. Theillustrated feature extractor overlays the object with a 5×5 grid andgenerates a vector that describes the shape of the object. Because thenumber of cells 53 in the grid does not change, the representativevector 51 is relatively stable when the size of the object (in number ofpixels) changes, such as when the object approaches or moves away fromthe camera. The vector 51 is concatenated with vectors 55 provided fromother feature extractors, if any, to produce a larger vector 57 thatrepresents the object. Other grid patterns and combinations of overlaysmay be used to achieve improved results based upon camera positionrelative to the vehicles and other environmental factors.

[0045] Masking using a vehicle template may be employed to removebackground information prior to feature extraction. The object is thencompared with templates 136 that depict the shape of known types ofvehicles such as cars, vans, trucks etc. When the best fit match isdetermined, the center of the object, where the center of the templateis located in the match position, is marked and only portions of theobject that are within the template are employed for generating thevectors.

[0046] The descriptive vectors generated by the feature extractors areprovided to a neural network classification engine that assigns a scoreto each object. The score indicates the probability of the object beinga vehicle, including the type of vehicle, e.g., passenger automobile,van, truck. Objects that produce a score that exceeds a predeterminedthreshold are determined to be vehicles of the type indicated. If thereare regions of overlap between objects in the updated object masterlist, ownership of the quanta in those regions is resolved in acompetition phase as depicted in step 60. Of the objects in competitionfor each quantum in the overlap region, the object that was assigned thehighest score by the neural network obtains ownership of the quanta.

[0047] Physical characteristics relating to object motion, such asvelocity, acceleration, direction of travel and distance betweenobjects, are calculated in step 62. The calculations are based onchanges in position of a plurality of quanta from frame to frame. Inparticular, vehicle velocity may be calculated as the average velocityof the quanta of the vehicle, by the change in location of a specificportion of the vehicle such as the center front, or by other techniques.Similarly, vehicle acceleration may be calculated as the change invehicle velocity over time and vehicle direction may be calculated byextrapolating from direction of travel of quanta over a plurality offrames. The velocity, acceleration and direction of travel of the quantaare calculated based on known length and width dimensions of each pixeland the known period of time between successive frames.

[0048] Referring to FIG. 8, the computation unit 12 includes at leastone video capture card 66. The video capture card 66 performs initialprocessing on video signals received from the camera 10. The computationunit 12 operates on the output of the video capture card 66. Thefunctions described with regard to the embodiment illustrated in FIGS. 8and 9 are implemented with a custom video capture card 66. Thesefunctions may alternatively be implemented with a commercially availableframe grabber and software. In the illustrative embodiment thecomputation unit 12 is a commercially available IBM compatible computerthat employs the Windows 95 operating system. The IBM compatiblecomputer includes a Peripheral Controller Interconnect (“PCI”) businterface.

[0049] Referring to FIG. 9, the video capture card 66 is operative toprocess new video frames, establish and maintain the reference frame,and compare the new frames with the reference frame in order toascertain luminance and/or edge differences therebetween that areindicative of motion. A digitizer circuit 70 is employed to convert theanalog video signals from the camera. The camera may provide analogvideo signals in either National Television Standards Committee (“NTSC”)format or Phase Alteration Line (“PAL”) format. The chrominance portion,if any, of the video signal is separated from the luminance portion ofthe video signal by the digitizer circuit 70. The resulting digitalsignals are provided to an image state machine 72 where the video signalis de-interlaced, if necessary. In particular, the video signal isde-interlaced unless a progressive scan camera is employed. The outputof the image state machine 72 is a succession of de-interlaced videoframes, each frame being 640 pixels by 480 pixels in size. The imagestate machine is coupled to a Random Access Memory (“RAM”) 74 thatincludes a ring of three buffers where frame data is collected prior totransmission of the frames over a digital bus 76 via pixel fetchcircuitry 78.

[0050] Referring to FIGS. 9 and 10, image stabilization is employed bythe video control processor 99 to compensate for camera movement due toenvironmental factors such as wind. Up to two anchor features 69 thatare identified by the user during configuration of the trafficmonitoring station are employed. The location of each anchor 69 on thenew frame 18 is determined, and the new frame is adjusted accordingly.Each anchor 69 is located by matching a template 162 to the image in thenew frame. The template 162 is a copy of the rectangular region of thereference frame 28 that includes a representation of the anchor feature69. A pixel by pixel comparison is made between the template 162 and aselected region of the new frame to determine whether a match has beenfound based on average luminance difference. The selected region of thenew frame is adjusted until the best match is located (bestmatch=_(minΣ)|New_(x,y)−Ref_(x,y)). The first comparison may be made atthe coordinates at which the anchor 69 is located in the referenceframe, or at the coordinates at which the anchor was located in theprevious frame. If a _(minΣ) calculation that is less than or equal tothe _(minΣ) calculation in the previous frame is found, the location isdetermined to be a match, i.e., the anchor is found. If a _(minΣ)calculation that is less than or equal to the _(minΣ) calculation in theprevious frame is not found, the location of the selected region isadjusted until the best match is located. The location of the selectedregion is adjustable within an area of up to 8 pixels in any directionfrom the matching coordinates of the previous frame. The selected regionis shifted in turn both vertically and horizontally by a distance offour pixels to yield four _(minΣ) calculation results. If the lowest ofthe four results is lower than the result at the start point, theselected region is moved to the place that yielded the lowest result. Ifnone of the results is lower than the result at the start point, theselected region is shifted in turn both vertically and horizontally byhalf the original distance, i.e., by two pixels, to yield four new_(minΣ) calculation results. If the lowest of the four results is lowerthan the result at the start point, the selected region is moved to theplace that yielded the lowest result. The distance may be halved againto one pixel to yield four new _(minΣ) calculation results. When thebest result is found, the anchor is considered found if the resultachieves a predetermined threshold of accuracy. If the best result failsto achieve the predetermined threshold of accuracy, an edge comparisonis undertaken. The edge comparison is made between the template and theregion that defines the best _(minΣ) calculation results. If at leastone vertical edge, at least one horizontal edge, and at least 75% of allconstituent edges are matched, the anchor is considered found.Otherwise, the anchor is considered not found.

[0051] The new frame 18 is adjusted to produce a stabilized frame basedupon how many anchors 69 were found, and where the anchors were found.In the event that both anchors are found and the anchors were found atthe same coordinates as in the reference frame, the camera did not moveand no correction is necessary. If both anchors moved by the samedistance in the same direction, a two-dimensional X-Y offset vector iscalculated. If both anchors moved in different directions, the cameramay have zoomed and/or rotated. A zoom is indicated when the anchorshave moved either towards or away from the center 164 of the image. Forexample, the anchors appear larger and further from the center of theimage when the camera zooms in, and smaller and closer to the center ofthe image when the camera zooms out. In the case of a camera that iszoomed out, the image is “inflated” by periodically duplicating pixelsso that the anchors appear in the expected dimensions. In the case of acamera that is zoomed in, the image is “deflated” by periodicallydiscarding pixels so that the anchors appear in the size that isexpected.

[0052] In the event that only one anchor was found, adjustment is basedupon the location of that one anchor. If the anchor did not move, nocorrection is applied. If the previous frame was not zoomed, it isassumed that the new frame is not zoomed. If the previous frame waszoomed, it is assumed that the new frame is also zoomed by the sameamount. In the event that neither anchor is found, the corrections thatwere calculated for the previous frame are employed.

[0053] From the number of anchors found and their positions, the videocontrol processor 99 calculates a set of correction factors as describedabove and sends them to the pixel fetch circuitry 78. These correctionfactors include instructions for shifting the frame horizontally,vertically, or both to correct for camera pan and tilt motion, and/ordirections for inflating or deflating the frame to compensate for camerazoom motion. If no correction is needed, the video control processorcalculates a set of correction factors which instructs the pixel fetchcircuitry to do a simple copy operation. The correction factors allowthe pixel fetch circuitry to select pixels from RAM 74 for transmissionon the bus 76 in stabilized order. The pixels are collected into astabilized frame 84 for use by the computation unit 12 (FIG. 8).

[0054] Referring to FIG. 9, a differencing unit 82 employs the contentsof the reference frame buffer 80 and the incoming pixels on the bus 76to compare the reference frame with the stabilized frame, pixel bypixel, in order to determine the differences. The difference values arestored in the difference frame buffer 86. The computation unit 12 mayaccess the difference frames over the PCI bus 94.

[0055] A tiling unit 88 is operative to organize the incoming pixels onbus 76 into tiles 22 (FIG. 3). The tiles are stored in a tile buffer 90for use by the computation unit 12 (FIG. 8), which may access the tilesvia the PCI bus 94.

[0056] Referring to FIG. 11, user-defined zones may be employed tofacilitate operation where the view of the camera is partiallyobstructed and where sections of roadway converge. An entry zone isemployed to designate an area of the video image in which new objectsmay be formed. Objects are not allowed to form outside of the entryzone. In the illustrated example, an overpass 100 partially obstructsthe roadway being monitored. By placing an entry zone 102 in front ofthe overpass 100, undesirable detection and tracking of vehiclestravelling on the overpass is avoided. A second entry zone 104 isdefined for a second section of roadway within the view of the camera.Vehicles entering the roadway through either entry zone 102 or entryzone 104 are tracked. An exit zone 106 is employed to designate an areawhere individual vehicles are “counted.” Because of the perspective ofthe field of view of the camera, more distant vehicles appear smallerand closer together. To reduce the likelihood of multiple vehicles beingcounted as a single vehicle, the number of vehicles included in thevehicle count is determined in the exit zone 106, which is proximate tothe camera.

[0057] Referring now to FIG. 12, a plurality of traffic monitoringstations 8 may be employed to monitor and share data from multiplesections of roadway. Information gathered from different sections ofroadway may be shared via a computer network. The gathered informationmay be displayed on a graphic user interface 108 located at a separateoperations center 110. Video images 112 from the camera are provided tothe graphic user interface 108 through flow manager software 114. Theflow manager maintains near actual time display of the video imagethrough balance of video smoothness and delay by controlling bufferingof video data and adapting to available bandwidth. Data resulting fromstatistical analysis of the video image is provided to the graphic userinterface 108 from an analysis engine 116 that includes the tiling unit,segmenter algorithms and neural network described above. The controllercard may be employed to transmit the data through an interface 118 tothe operations center 110, as well as optional transmission to othertraffic monitoring stations. The interface 118 may be shared memory inthe case of a standalone monitoring station/graphic user interfacecombination or sockets in the case of an independent monitoring stationand graphic user interface. The operations center 110 contains anintegration tool set for post-processing the traffic data. The tool setenables presentation of data in both graphical and spreadsheet formats.The data may also be exported in different formats for further analysis.The video may also be displayed with an overlay representing vehicleposition and type.

[0058] Alarm parameters may be defined for the data generated by theanalysis engine 116. For example, an alarm may be set to trigger if theaverage velocity of the vehicles passing through the field of view ofthe camera drops below a predetermined limit. Alarm calculations may bedone by an alarm engine 122 in the traffic monitoring station or at thegraphic user interface. Alarm conditions are defined via the graphicuser interface.

[0059] Networked traffic monitoring stations may be employed to identifyand track individual vehicles to determine transit time betweenstations. The shape of the vehicle represented by active tiles isemployed to distinguish individual vehicles. At a first traffic controlstation, a rectangular region (“snippet”) that contains the active tilesthat represent a vehicle is obtained as depicted by step 132. Correctionmay be made to restore detail obscured by inter-field distortion asdepicted by step 134. The snippet is then compared with templates 136that depict the shape of known types of vehicles such as cars, vans,trucks etc, as depicted in step 138. When the best fit match isdetermined, the center of the snippet, where the center of the templateis located in the match position, is marked as depicted by step 140.Further, the size of the snippet may be reduced to the size of thematching template. First and second signatures that respectivelyrepresent image intensity and image edges are calculated from thesnippet as depicted by step 142. The signatures, matching template type,vehicle speed and a vehicle lane indicator are then transmitted to asecond traffic monitoring station as depicted by step 144. The secondtraffic monitoring station enters the information into a list that isemployed for comparison purposes as depicted in step 146. As depicted bystep 148, information that represents vehicles passing the secondtraffic monitoring station is calculated by gathering snippets ofvehicles and calculating signatures, a lane indicator, speed and vehicletype in the same manner as described with respect to the first trafficmonitoring station. The information is then compared with entriesselected in step 149 from the list by employing comparitor 150. Inparticular, entries that are so recent that incredibly high speed wouldbe required for the vehicle to be passing the second traffic monitoringstation are not employed. Further, older entries that would indicate anincredibly slow travel rate are discarded. The signatures may beaccorded greater weight in the comparison than the lane indicator andvehicle type. Each comparison yields a score, and the highest score 152is compared with a predetermined threshold score as depicted by step154. If the score does not exceed the threshold, the “match” isdisregarded as depicted by step 156. If the score exceeds the threshold,the match is saved as depicted by step 158. At the end of apredetermined interval of time, a ratio is calculated by dividing thedifference between the best score and the second best score by the bestscore as depicted by step 160. If the ratio is greater than or equal toa predetermined value, a vehicle match is indicated. The transit timeand average speed of the vehicle between traffic monitoring stations isthen reported to the graphic user interface.

[0060] Inter-field distortion is a by-product of standard video camerascanning technology. An NTSC format video camera will alternately scaneven or odd scan lines every 60th of a second. A fast moving vehiclewill move enough during the scan to “blur,” seeming to partially appearin two different places at once. Typically, the car will move about 1.5ft during the scan (approx. 60 mph). Greater distortion is observed whenthe car travels at higher velocity. Greater distortion is also observedwhen the vehicle is nearer to the camera. The distortion compensatingalgorithm is based on knowledge of the “camera parameters” and the speedof the vehicle. The camera parameters enable mapping between motion inthe real world and motion in the image plane of the camera. Thealgorithm predicts, based on the camera parameters and the known speedof the vehicle, how much the vehicle has moved in the real world (in thedirection of travel). The movement of the vehicle on the image plane isthen calculated. In particular, the number of scan lines and distance tothe left or right on the image is calculated. Correction is implementedby moving the odd scan lines ‘back’ to where the odd scan lines wouldhave been if the car had stayed still (where the car was when the evenscan lines were acquired). For example, to move 4 scan lines back, scanline n would be copied back to scan line n−4, where n is any odd scanline. The right/left movement is simply where the scan line ispositioned when copied back. An offset may be added or subtracted tomove the pixels back into the corrected position. For example, scan linen may have an offset of 8 pixels when moved back to scan line n−4, sopixel 0 in scan line n is copied to pixel 7 in scan line n−4, etc. Ifthe speed of a particular vehicle cannot be determined, the averagespeed for that lane may be employed to attempt the correction.Distortion correction is not necessary when a progressive scan camera isemployed.

[0061] Referring to FIG. 14, the traffic monitoring station may beemployed to facilitate traffic control. In the illustrated embodiment,the traffic monitoring station is deployed such that an intersection iswithin the field of view of the camera. Vehicle detection can beemployed to control traffic light cycles independently for left andright turns, and non-turning traffic. Such control, which would requiremultiple inductive loops, can be exerted for a plurality of lanes with asingle camera. Predetermined parameters that describe vehicle motion areemployed to anticipate future vehicle motion, and proactive action maybe taken to control traffic in response to the anticipated motion of thevehicle. For example, if the traffic monitoring station determines thata vehicle 124 will “run” a red light signal 125 by traversing anintersection 126 during a period of time when a traffic signal 128 willbe indicating “green” for a vehicle 130 entering the intersection fromanother direction, the traffic monitoring station can provide a warningor control such as an audible warning, flashing light and/or delayedgreen light for the other vehicle 130 in order to reduce the likelihoodof a collision. Further, the traffic monitoring station may track theoffending vehicle 124 through the intersection 126 and use the trackinginformation to control a separate camera to zoom in on the vehicleand/or the vehicle license plate to record a single frame, multipleframes or a full motion video movie of the event for vehicleidentification and evidentiary purposes. The cameras are coordinated viashared reference features in a field of view overlap area. Once thesecond camera acquires the target, the second camera zooms in to recordthe license plate of the offending vehicle. The traffic monitoringstation could also be used to detect other types of violations such asillegal lane changes, speed violations, and tailgating. Additionally,the traffic monitoring station can be employed to determine the optimaltimes to cycle a traffic light based upon detected gaps in traffic andlengths of queues of cars at the intersection.

[0062] The determination of whether the vehicle will run the red lightmay be based upon the speed of the vehicle and distance of the vehiclefrom the intersection. In particular, if the vehicle speed exceeds apredetermined speed within a predetermined distance from theintersection it may be inferred that the vehicle cannot or is unlikelyto stop before entering the intersection.

[0063] As described herein, the disclosed system employs video trackingto detect vehicles that will not stop for a traffic light that ischanging to red. In an illustrative embodiment, the disclosed systemoutputs a signal in response to detection of such Non-Stopping Vehicles(NSV's), for example when they have completed passage through theintersection and cross traffic can safely proceed. With thisinformation, a traffic controller can be optionally programmed to delaythe onset of the green light for cross traffic or pedestrians until anydetected red-light violating vehicles have moved through theintersection. Through its ability to anticipate red-light vehicleviolation before the violation occurs, the disclosed system mayadvantageously reduce the risk of collisions and/or injuries atintersections.

[0064] The features of the presently disclosed system involve theability (1) to process a sequence of video images of oncoming traffic todetect, classify and provide continuous tracking of detected vehicles;(2) to calculate from image information real world vehicle displacementswith sufficient accuracy to support measurement of vehicle speed andacceleration for all vehicles in the camera field of view; (3) toprovide continuous, real time measurement of vehicle position, speed andacceleration; (4) to develop and implement a decision model, using amongits inputs, measures of vehicle position, speed, acceleration andclassification in order to determine likelihood of a vehicle stopping;and (5) to update the decision model in real time as a result ofchanging values of vehicle parameters (e.g., position, speed,acceleration) for each oncoming vehicle in the camera's field of view.The disclosed system supports the use of cameras for intersectionmonitoring/control and surveillance. The disclosed system may further beapplied to other safety considerations: the detection of vehiclesapproaching toll booths, railway crossings or other controlled roadwaystructures (lane reducers, etc.) at excessive speeds, and/or thedetection of vehicles exhibiting other aspects of hazardous drivingbehavior.

[0065] The disclosed system includes innovative video-monitoringcapabilities to improve the safety of signalized intersections. Asignificant risk exists for motorists who are given a green light toproceed through an intersection when a vehicle oncoming from anotherdirection of travel elects to run the red-light for that direction.Using the disclosed system, Non-Stopping Vehicles (NSV's) can bedetected by a sensor in advance of the onset of the green light for thecross traffic, and the sensor can generate a signal that specificallyindicates a condition of a vehicle passing or about to pass through anintersection in violation of a red-light. This signal can then be usedto delay the onset of the green light phase of a traffic light for anycross traffic until the NSV has moved through the intersection and it issafe for cross traffic and/or pedestrians to proceed. In an illustrativeembodiment, such a sensor signal is not necessarily used to dynamicallylengthen, or otherwise change, the timing cycle for the traffic light ofthe NSV. The timing cycle in the traffic light for the NSV may be leftunchanged and the NSV does indeed violate a red-light.

[0066] The capabilities of the disclosed system are based on monitoringand controlling traffic flow at an intersection through approachdetection, stop line and turn detection functionality. The disclosedsystem accurately provides speed and position information on vehicleswithin a field of view, and determines from the motion characteristicsof oncoming vehicles and a knowledge of the intersection timing cycle,whether a vehicle is in the process of running a red-light or has a highprobability of running the red-light. In one embodiment, thisdetermination starts as soon as the light for traffic in the givendirection of interest cycles to yellow. The accurate, early detection ofan NSV provided by the disclosed system is essential in order to producea sensor signal in advance of the normal cycle for activating the greenlight of the cross traffic.

[0067] The disclosed system supports a number of options for determiningan “all clear” signal for an intersection. For example, in intersectionsequipped with high slew rate PTZ cameras (>90 deg/sec.), the camera thatimages the NSV can be controlled by the disclosed system to track theNSV through the intersection, generating the all clear signal when theNSV has exited the intersection. Alternatively, for intersections notequipped with PTZ cameras, two alternative embodiments may be employedto determine the “all clear” signal. In a first such alternativeembodiment, no single camera images the entire intersection area, butthe intersection is completely imaged in the field of view of two ormore cameras. In this first alternative embodiment, the multiple camerasmay be used to cooperatively track the target NSV through the field ofview of each camera until the NSV has traversed the entire intersection.In a second alternative embodiment, in which the intersection is notimaged either by a single camera or by combined multiple cameras, adetermination is made, on the basis of the NSV's speed and accelerationas it exits the camera's field of view, and from predetermined knowledgeof the spatial extent of the intersection that is not imaged either by asingle camera or by multiple cameras, of the time that will elapsebefore the vehicle has moved completely through the intersection. Basedon such a determination, the all clear signal can be generated once thispredicted time has elapsed. This second alternative embodiment requiresthe fewest resources in terms of camera installation requirements.

[0068] In an exemplary embodiment, the present invention may, forexample, provide (1) wide area detection of vehicles entering theapproach zone of an intersection, (2) high-precision continuous speedand acceleration profiles using 20 frame-per-second video sampling; (3)single camera support for monitoring two approach directions, (4)continuous tracking of vehicles as they cross lane boundaries, and (5)cooperative vehicle tracking between multiple cameras to ensure vehicleclearance through the intersection area.

[0069] The disclosed system advantageously operates to anticipate andpredict traffic light violations before they occur, for example using anapproaching vehicle's speed. Accordingly, the disclosed system can beused to intelligently delay a green light until the offending vehicle issafely past the intersection. Along these same lines, one or more videocameras may be used by the present system because they meet keyrequirements of the intersection safety application: the need for sensorinformation on which accurate measurements of vehicle position, speed,acceleration and classification can be made; the need for suchinformation on all vehicles approaching the intersection (not just forthose that are nearest to the intersection in each lane); and the needfor continuously updated information such that the individual vehiclemeasurements can be continuously updated in real time.

[0070] Alternatively, other sensor technologies may be used as the basisfor sensor devices in the disclosed system, provided that they canpotentially satisfy the sensor requirements for the intersection safetyapplication. For example, the more advanced synthetic aperture radar(SAR) systems may be used to provide a radar-based image of the roadway.In similar fashion to video processing, a radar-based image or imagescan be processed to detect, track and classify target vehicles on acontinuous basis.

[0071] As a further advantageous feature, the present system leveragesthe investment that municipalities make in deploying video cameras atintersections for surveillance and/or traffic monitoring and flowcontrol purposes to provide significant safety benefits as well. Becausethe disclosed system potentially supports such dual use of videocameras, significant cost savings result. Additionally, because of thecompatibility of the disclosed system with surveillance uses of thevideo cameras, it can be used for active monitoring in a traffic controlcenter, offering the potential for integration with larger trafficmanagement systems.

[0072] Moreover, the features of the disclosed system can provideintelligent green light delays based upon the detection of pedestriansthat are in the process of crossing an intersection at a well definedcrosswalk. In some cases, late-crossing pedestrians are not visible tomotorists who are waiting for a green light. This may result from amotorist's view being obstructed by a truck or bus in an adjacent lane.An embodiment of the disclosed system could identify pedestrians orpedestrian groups, and communicate information on expected time tocrossing completion that could be optionally used to delay activation ofa green light until such pedestrians were safely clear of theintersection. The disclosed system may further be embodied to monitorother potential safety hazards, such as: vehicles moving at excessivespeed approaching toll booths or other controlled roadway structures(lane reducers, etc.); vehicles out of control on steep grades, hills orsteep downgrades leading to a traffic light or rail crossing.Additionally, the disclosed system can use the tracking positioninformation it generates to control and move a high performance PTZcamera mechanism and standard NTSC color camera to zoom in on andidentify a potential red-light violator for enforcement purposes. Insuch a case, no additional pavement sensors or fixed cameras arerequired for red-light violation monitoring.

[0073] The principles of the disclosed system may be applied to anyintersection monitoring system, whether video-based or not, that meetsrequired minimum operational specifications for the creation of accuratevehicle count and approach speed measurements on a real-time basis.Irrespective of the specific sensor technology employed, controllerlogic must be employed to interface sensor outputs to the intersectiontraffic lights. For example, some existing controllers can be adapted toimplement a green-light delay based on outputs from the disclosedsystem.

[0074] Implementations and/or deployments of the disclosed system coulddiffer in operation from intersection to intersection. In this regard,an implementation of the disclosed system could be configured to takeinto account the different demographics of an area as well as the natureof its traffic flow patterns. For example, at rush hour, vehicles often“creep” into an intersection on a yellow light, even when traffic isbacked up, preventing their transit through the intersection before theonset of a red light in their direction. In such cases, the disclosedsystem may be pre-configured to delay the activation of the crosstraffic green light only for a maximum period of time, in order to givecross traffic the opportunity to flow, allowing them to navigate aroundvehicles present in the intersection but which have not yet traversedthe intersection region.

[0075] Having described the embodiments consistent with the presentinvention, other embodiments and variations consistent with the presentinvention will be apparent to those skilled in the art. Therefore, theinvention should not be viewed as limited to the disclosed embodimentsbut rather should be viewed as limited only by the spirit and scope ofthe appended claims. short PTLPivotSolve ( double XOffset, doubleYOffset, double Lane Width, double Lanes, double LSlope, double RSlope,double DiffX, double CameraHeight, double Grade, double Bank, doublexPoint, double yPoint, double RealX, double RealY, double *LambdaSolsdouble *panSols, double *TiltSols, double *Error, short ArraySize ) {short SolCount; long x, y; double Xo, Yo, LinearDistance, yP; doubleHorizon, New Horizon, dim; double BaseTilt, BaseLambda, BasePan, PivotX,PivotY, Tilt, Pan, Lambda; CameraParams  cp; double Scaler = 240.0;double PI = 3.1415926535; double RadialDistance; SolCount = 0; cp.Height= CameraHeight; cp.XSize = 640; cp.YSize = 480; cp.XCenter = 320;cp.YCenter = 240; cp.XOffset = (long) XOffset; cp.YOffset = (long)YOffset; Grade = atan ( Grade / 100.0 ); Bank = atan ( Bank / 100.0 );cp.Grade = Grade; cp.sinG = sin ( Grade ); Yo = CameraHeight * cp.sinG;cp.cosG = cos ( Grade ); CameraHeight = CameraHeight * cp.cosG; cp.Bank= Bank; cp.sinB = sin ( Bank ) ; Xo = CameraHeight * cp.sinB; cp.cosB =cos ( Bank ); CameraHeight = CameraHeight * cp.cosB; DiffX /= Scaler;dim = 1.0 /RSlope; dim−= 1.0 /LSlope; dim /= Lanes; Horizon =−DiffX /(Lanes * dim); SolCount = 0; LinearDistance = sqrt ( ( RealX * RealX ) +(RealY * RealY ) ); BaseTilt = atan ( CameraHeight /LinearDistance ); if( _isnan ( BaseTilt ) ) { // Bogus solution return ( 0 ); // Nosolutions } cp.st = sin(BaseTilt); cp.ct = cos(BaseTilt); yP = (240 −yPoint − YOffset) / Scaler; NewHorizon = Horizon − ( yP); BaseLambda =NewHorizon / tan (BaseTilt ); if (_isnan ( BaseLambda ) ) { return ( 0); } cp.Lambda = BaseLambda; RadialDistance=sqrt ((LinearDistance*LinearDistance) + (CameraHeight *CameraHeight)); BasePan= atan (RealX / RadialDistance); if (_isnan ( BasePan ) ) { return ( 0); } cp.sp = sin(BasePan); cp.cp = cos(BasePan); x = 640 − (long)xPoint; y − 480 − (long) yPoint; GetRealXY ( &cp, x, y, &PivotX, &PivotY); // Now get the real, relocated camera parameters LinearDistance =sqrt (( PivotX * PivotX) + ( PivotY * PivotY)); RadialDistance=sqrt((LinearDistance*LinearDistance) + (CameraHeight *CameraHeight)); Tilt =atan ( CameraHeight / LinearDistance ); if (_isnan ( Tilt ) ) { // Bogussolution return ( 0 ); // No solutions } Lambda = (Horizon /CameraHeight) *RadialDistance; if (_isnan ( Lambda ) ) { // Bogussolution return ( 0 ); // No solutions } Pan = asin ( PivotX) /RadialDistance ); if (_isnan ( Pan ) ) { // Bogus solution return ( 0 );// No solutions } (*LambdaSols = Lambda; *PanSols = Pan; *TiltSols =Tilt; *Error = 0.0; SolCount = 1; return ( SolCount ); }

[0076]

What is claimed is:
 1. A traffic light violation prediction system for atraffic signal having a at least a red phase and a green phase,comprising: at least one image capturing device, said image capturingdevice operative to provide image data of at least one vehicleapproaching said traffic signal; and a computation unit, operative inresponse to said image capturing device and an indication of saidcurrent traffic light phase, to determine whether said at least onevehicle approaching said traffic signal will violate a red light phaseof said traffic signal.
 2. The system of claim 1, wherein said imagecapturing device comprises at least one video camera.
 3. The system ofclaim 1, wherein said traffic signal has a yellow light phase, and saidcomputation unit is further responsive to a time remaining in saidyellow light phase.
 4. The system of claim 1, wherein said computationunit is further responsive to a current speed of said at least onevehicle approaching said traffic intersection.
 5. The system of claim 1,wherein said computation unit is further responsive to a currentacceleration of said at least one vehicle approaching said trafficintersection.
 6. The system of claim 1, wherein said computation unit isfurther responsive to a current position of said at least one vehicleapproaching said traffic intersection.
 7. The system of claim 1, whereinsaid computation unit is further operable to compute a time remainingbefore said at least one vehicle approaching said traffic intersectionenters said traffic intersection, responsive to a determination of acurrent acceleration of said vehicle.
 8. The system of claim 7, whereinsaid computation unit is further operable to calculate a rate ofdeceleration required for said at least one vehicle to stop within saidtime remaining before said vehicle enters said traffic intersection. 9.A method for predicting a traffic light violation of a traffic signalhaving at least a red phase and a green phase, comprising: providingimage data showing at least one vehicle approaching said traffic signal;and determining, responsive to said image data and an indication of acurrent traffic light phase, whether said at least one vehicleapproaching said traffic signal will violate a red light phase of saidtraffic signal.
 10. The method of claim 9, wherein said image data isgenerated by at least one video camera.
 11. The method of claim 9,wherein said determining is performed by a computation unit comprisingsoftware executing on a processor.
 12. The method of claim 9, whereinsaid traffic light further includes a yellow phase, and wherein saiddetermining is further responsive to a time remaining in said yellowphase.
 13. The method of claim 9, wherein said determining furtherincludes the step of determining a current speed for said at least onevehicle approaching said traffic intersection.
 14. The method of claim9, wherein said determining further includes the step of determining acurrent acceleration for said vehicle approaching said trafficintersection.
 15. The method of claim 14, wherein said determiningfurther includes computing a time remaining before said vehicleapproaching said traffic intersection enters said traffic intersection,responsive to said determination of said current acceleration of saidvehicle.
 16. The method of claim 15, further comprising calculating, bysaid computation unit, a deceleration required for said vehicle to stopwithin said time remaining before said vehicle enters said trafficintersection.
 17. A collision avoidance system for a first trafficsignal having a current light phase equal to one of a red light phaseand a green light phase, and a second traffic signal having a currentlight phase equal to one of a red light phase and a green light phase,comprising: at least one image capturing device, for capturing aplurality of images; said plurality of images showing at least onevehicle approaching said first traffic signal; a computation unit,responsive to said plurality of images and an indication of said currentfirst traffic signal light phase, for determining whether said at leastone vehicle approaching said first traffic signal will violate said redlight phase of said first traffic signal, and for delaying an upcominggreen traffic light phase of said second traffic signal responsive to adetermination that said at least one vehicle approaching said firsttraffic signal will violate a red phase of said first traffic signal.18. The system of claim 17, wherein said image capturing devicecomprises at least one video camera.
 19. The system of claim 17, whereinsaid computation unit comprises software executing on a processor. 20.The system of claim 17, wherein said computation unit is furtherresponsive to a time remaining in yellow light phase input.
 21. Thesystem of claim 17, wherein said computation unit is further operable todetermine a current speed for said at least one vehicle.
 22. The systemof claim 1, wherein said computation unit is further operable todetermine a current acceleration for said at least one vehicle.
 23. Thesystem of claim 17, wherein said computation unit is further operable tocompute a time remaining before one of said at least one vehicle enterssaid traffic intersection, responsive to determination of a currentacceleration of said vehicle.
 24. The system of claim 23, wherein saidcomputation unit is further operable to calculate a decelerationrequired for said at least one vehicle to stop within said timeremaining before said vehicle enters said traffic intersection.
 25. Amethod of collision avoidance for a first traffic signal having acurrent light phase equal to one of the set including at least red andgreen and a second traffic signal having a current light phase equal toone of the set including at least red and green, comprising: capturing aplurality of images, said images showing at least one vehicleapproaching said first traffic signal, said images derived from anoutput of a violation prediction image capturing device; determining,responsive to said plurality of images and indication of said currentfirst traffic signal light phase, whether said at least one vehicleapproaching said first traffic signal will violate a red light phase ofsaid first traffic signal; and delaying an upcoming green light phase ofsaid second traffic signal for a programmed time period responsive to adetermination that said at least one vehicle approaching said firsttraffic signal will violate said red light phase of said first trafficsignal.
 26. The method of claim 25, wherein said violation predictionimage capturing device comprises at least one video camera.
 27. Themethod of claim 25, wherein said collision avoidance unit comprisessoftware executing on a processor.
 28. The method of claim 25, furthercomprising: determining at least one vehicle location associated withsaid at least one vehicle; and wherein said determining whether said atleast one vehicle will violate said red light phase of said firsttraffic signal is responsive to said at least one vehicle location. 29.The method of claim 25, further comprising: determining a time remainingin a current yellow light phase; and wherein said determining whethersaid at least one vehicle will violate said red light phase of saidfirst traffic signal is responsive to said time remaining in saidcurrent yellow light phase.
 30. The method of claim 25, furthercomprising: determining a current speed for said at least one vehicle;and wherein said determining whether said at least one vehicle willviolate said red light phase of said first traffic signal is responsiveto said current speed of said at least one vehicle.
 31. The method ofclaim 25, wherein said determining whether said at least one vehiclewill violate said red light phase of said first traffic signal furthercomprises determining a current acceleration for said at least onevehicle.
 32. The method of claim 25, wherein said determining whethersaid at least one vehicle will violate said red light phase of saidfirst traffic signal further comprises computing a time remaining beforesaid at least one vehicle enters said traffic intersection.
 33. Themethod of claim 32, wherein said determining whether said at least onevehicle will violate said red light phase of said first traffic signalfurther comprises calculating a rate of deceleration required for saidat least one vehicle to stop within said time remaining before saidvehicle enters said traffic intersection.
 34. The method of claim 33,wherein said determining whether said at least one vehicle will violatesaid red light phase of said first traffic signal further comprisesdetermining whether said required deceleration is larger than aspecified deceleration limit value.
 35. An apparatus for facilitatingoperation of a traffic light at an intersection of first and secondroadways, comprising: at least one camera that provides first and secondvideo frames that are representative of a field of view of said cameraat different points in time; a video processing circuit that detectsvehicles; and a processor circuit that determines vehicle position inthe first and second video frames and vehicle velocity from thedifference in vehicle position, said processor circuit being operativeto delay the operation of said traffic light in response topredetermined conditions associated with said first and second videoframes.
 36. An apparatus for facilitating operation of a traffic lightat an intersection of first and second roadways, comprising: at leastone camera that provides first and second video frames that arerepresentative of a field of view of said camera at different points intime; a video processing circuit that detects vehicles; and a processorcircuit that determines vehicle position in the first and second videoframes and vehicle velocity from the difference in vehicle position,said processor circuit being operative to generate a signal indicatingthat a vehicle is about to pass through said intersection in violationof a red-light.
 37. A method for controlling vehicular traffic at anintersection having a first traffic light for controlling trafficapproaching said intersection from a first direction and a secondtraffic light for controlling traffic approaching said intersection froma second direction, wherein each of said traffic lights includes atleast a red phase and a green phase, said method comprising: obtaining aplurality of images of a vehicle approaching said intersection from saidfirst direction; analyzing said plurality of images to predict whethersaid vehicle will violate said red light phase of said first trafficlight; responsive to a determination said vehicle will violate said redlight phase of said first traffic light generating a delay signalindicative of said predicted violation.
 38. The method of claim 37further including delaying a change of said second traffic signal fromsaid red light phase to said green light phase responsive to said delaysignal.
 39. The method of claim 37, wherein said obtaining saidplurality of images is performed by an image capturing device comprisingat least one video camera.
 40. The method of claim 37, wherein analyzingsaid plurality of images is performed by a collision avoidance unitcomprising software executing on a processor.
 41. The method of claim37, further comprising: determining at least one vehicle locationassociated with said at least one vehicle; and wherein said determiningwhether said at least one vehicle will violate said red light phase ofsaid first traffic signal is responsive to said at least one vehiclelocation.
 42. The method of claim 37, further comprising: determining atime remaining in a current yellow light phase; and wherein saiddetermining whether said at least one vehicle will violate said redlight phase of said first traffic signal is responsive to said timeremaining in said current yellow light phase.
 43. The method of claim37, further comprising: determining a current speed for said at leastone vehicle; and wherein said determining whether said at least onevehicle will violate said red light phase of said first traffic signalis responsive to said current speed of said at least one vehicle. 44.The method of claim 37, wherein said determining whether said at leastone vehicle will violate said red light phase of said first trafficsignal further comprises determining a current acceleration for said atleast one vehicle.
 45. The method of claim 37, wherein said determiningwhether said at least one vehicle will violate said red light phase ofsaid first traffic signal further comprises computing a time remainingbefore said at least one vehicle enters said traffic intersection. 46.The method of claim 45, wherein said determining whether said at leastone vehicle will violate said red light phase of said first trafficsignal further comprises calculating a rate of deceleration required forsaid at least one vehicle to stop within said time remaining before saidvehicle enters said traffic intersection.
 47. The method of claim 46,wherein said determining whether said at least one vehicle will violatesaid red light phase of said first traffic signal further comprisesdetermining whether said required deceleration is larger than aspecified deceleration limit value.
 48. An accident avoidance system foran intersection having a traffic signal having a current light phaseequal to one of a red light phase and a green light phase, and apedestrian crosswalk passing through a path of traffic controlled bysaid first traffic signal, comprising: at least one image capturingdevice, for capturing a plurality of images of said pedestriancrosswalk, said plurality of images showing at least one pedestrianwithin said pedestrian crosswalk; and a computation unit, responsive tosaid plurality of images and an indication of said current trafficsignal light phase, for determining whether said at least one pedestrianin said crosswalk will exit said crosswalk prior to said current trafficsignal light phase transitioning from red to green, and for delaying anupcoming green traffic light phase of said traffic signal responsive toa determination that said at least one pedestrian will not exit saidcrosswalk prior to said current traffic signal light phase transitioningfrom red to green.
 49. The system of claim 48, wherein said imagecapturing device comprises at least one video camera.
 50. The system ofclaim 48, wherein said computation unit is further responsive to acurrent speed of said at least one pedestrian within said pedestriancrosswalk.
 51. The system of claim 48, wherein said computation unit isfurther responsive to a current acceleration of said at least onepedestrian within said pedestrian crosswalk.
 52. The system of claim 48,wherein said computation unit is further responsive to a currentposition of said at least one pedestrian within said pedestriancrosswalk.
 53. The system of claim 48, wherein said computation unit isfurther operable to compute a time remaining before said current trafficsignal light phase transitions from red to green.